Floorplan reconstruction
turns raw sensor data (e.g.  point clouds, density maps, RGB images) into vectorized geometries.
Early methods rely on basic image processing techniques, e.g. Hough transform or plane fitting [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}.
Graph-based methods [8]}, [9]}, [10]} cast floorplan reconstruction as an energy minimization problem.
Recent deep learning methods replace some hand-crafted components with neural networks.
Typical top-down methods such as
Floor-SP[11]} rely on Mask R-CNN[12]} to detect room segments and reconstruct polygons of individual room segments by sequentially solving shortest path problems.
Similarly, MonteFloor [13]} first detects room segments and then relies on Monte-Carlo Tree-Search to select room proposals. Alternative bottom-up methods, such as
FloorNet [14]} first detect room corners, followed by an integer programming formulation to generate wall segments. This approach, however is limited to Manhattan scenes.
Recently, HEAT [15]} proposed an end-to-end model following a
typical bottom-up pipeline:
first detect corners, then classify edge candidates between corners.
Although end-to-end trainable, it cannot recover edges from undetected corners. Instead, our approach skips the heuristics-guided processes from both approaches. Without explicit corner, wall or room detection,
our approach directly generates rooms as polygons in a holistic fashion.
